library(tidyverse)
library(here)
library(readr)
library(readxl)
library(data.table)
full_data <-
read_csv(here("clean_data/birds_clean.csv"))
"raw_data/seabirds.xls" %>%
here() %>%
read_excel() %>%
data.table()
full_data %>%
data.table()
Finding the bird with the most individual sightings
highest_individual_sightings <- full_data %>%
count(species_abbreviation) %>%
top_n(1)
## Selecting by n
highest_individual_sightings2 <- left_join(highest_individual_sightings, full_data, by = "species_abbreviation")
highest_individual_sightings2 %>%
select(n, species_abbreviation, species_common_name_taxon_age_sex_plumage_phase, species_scientific_name_taxon_age_sex_plumage_phase) %>%
head(1)
Finding the bird with the highest total count?
highest_total_count <- full_data %>%
group_by(species_abbreviation) %>%
summarise(sum(count)) %>%
top_n(1)
highest_total_count2 <- left_join(highest_total_count, full_data, by = "species_abbreviation")
highest_total_count2 %>%
select("sum(count)", species_abbreviation, species_common_name_taxon_age_sex_plumage_phase, species_scientific_name_taxon_age_sex_plumage_phase) %>%
head(1)
Finding bird with the highest total count above a latitude of -30?
highest_total_count_above_lat_30 <- full_data %>%
filter(lat > -30) %>%
group_by(species_abbreviation) %>%
summarise(sum(count)) %>%
top_n(1)
highest_total_count_above_lat_30_again <- left_join(highest_total_count_above_lat_30, full_data, by = "species_abbreviation")
highest_total_count_above_lat_30_again %>%
select("sum(count)", species_abbreviation, species_common_name_taxon_age_sex_plumage_phase, species_scientific_name_taxon_age_sex_plumage_phase) %>%
head(1)
How many different types of birds were only ever seen in groups of 1?
full_data %>%
filter(count < 2) %>%
select(species_common_name_taxon_age_sex_plumage_phase, species_scientific_name_taxon_age_sex_plumage_phase, species_abbreviation) %>%
distinct() %>%
arrange(species_abbreviation) %>%
count()
How many penguins were seen? (N.B. there are many types of penguin)
full_data %>%
filter(str_detect( species_common_name_taxon_age_sex_plumage_phase, "penguin")) %>%
summarise(sum(count,na.rm = TRUE))